Survdat Abundance and Biomass Check
Adam A. Kemberling
2021-03-31
Species Abundance and Biomass Check for 2019 Survdat
While investigating some discrepancies among different datasets a handful of corrections were made. This document is intended to see if any of those changes have important implications for some of the species-specific reports that have been done recently.
This report compares two datasets:
1. survdat_Nye_allseasons.RData = 2019 Survdat
2. NEFSC_BTS_all_seasons_03032021.RData = March 2021 Survdat
Load SURVDAT Sources
# Cleanup functions
source(here("R/01_nefsc_ss_build_nodrop.R"))
# 2019 Data used in 2020 paper
load(paste0(nmfs_path, "Survdat_Nye_allseason.RData"))
survdat_nye <- survdat %>% clean_names()
# Data we just received in 2021 with errors located and corrected
load(paste0(nmfs_path, "2021_survdat/NEFSC_BTS_all_seasons_03032021.RData"))
survdat_21 <- survey$survdat %>% clean_names()Run Standard Cleanup
# Run cleanup
survdat_nye <- survdat_prep_nodrop(survdat = survdat_nye) %>%
mutate(survdat_source = "survdat_nye")
# Run cleanup
survdat_21 <- survdat_prep_nodrop(survdat = survdat_21) %>%
mutate(survdat_source = "survdat_2021")
rm(survdat, survey)Load List of Published Species
#### Load the species list from Andrew
species_check <- read_csv(here("data/andrew_species/Assesmentfishspecies.csv"),
col_types = cols())
species_check <- species_check %>%
clean_names() %>%
mutate(svspp = str_pad(svspp, 3, pad = "0", side = "left"),
comname = tolower(comname),
species = str_to_title(species)) %>%
arrange(svspp)
# Put in a list
source_list <- list("survdat_nye_2019" = survdat_nye,
"survdat_2021" = survdat_21)Making Comparisons
In this code chunk the different sources are put together in a list, and the same cleanup steps are applied to each. The years are limited to before 2017 so there are no extra years in some not available in others. The SVSPP codes are also limited to just the ones in the list Andrew provided.
After that repeated records for abundance and biomass are dropped. These repeats exist to accommodate the length and number at length details. We don’t need them for this so they are dropped here.
# Make years comparable
# Filter species down for both
source_list <- map(source_list, function(survdat_data){
# filter years
sdat <- survdat_data %>%
filter(est_year >= 2008,
est_year <= 2017,
svspp %in% species_check$svspp)
# Pull distinct station abundance and biomass for each species
sdat <- sdat %>%
distinct(id, est_year, season, svvessel,
comname, svspp, catchsex, abundance, biom_adj) })
# Split each survdat set into a list of single species tables
source_splits <- map(source_list, function(source_data){
source_split <- source_data %>% split(.$comname) })To make comparisons between the different data sets I first check that the svspp code is present in the dataset. Then for each source I get a total annual value for biomass and abundance. Then across data sources I calculate the correlation between these values, and the percent change from one data source to the most recently revised data pull.
The table itself is retained in addition to some overall summary metrics.
# Make Comparisons
# Vector of species and their common names, atleast the common names Andrew used
andrew_species <- species_check$comname %>% setNames(species_check$svspp)
# Pulling out details for the species
species_comparisons <- imap(andrew_species, function(species_comname, species_svspp){
# there are some name mismatches in common name, so catch those and return an error
# that explains which data it was not in
in_nye <- species_comname %in% names(source_splits$survdat_nye_2019)
in_21 <- species_comname %in% names(source_splits$survdat_2021)
in_both <- in_nye & in_21
if(in_both == FALSE){
return(list("in_nye" = in_nye,
"in_21" = in_21,
"data" = data.frame(),
"metrics" = data.frame())) }
# Pull just that species from the 2019 data
summ_19 <- source_list$survdat_nye_2019 %>%
filter(svspp == species_svspp) %>%
group_by(comname, est_year) %>%
summarise(abund_19 = sum(abundance, na.rm = T),
biom_19 = sum(biom_adj, na.rm = T),
.groups = "keep") %>% ungroup()
# and 2021 data
summ_21 <- source_list$survdat_2021 %>%
filter(svspp == species_svspp) %>%
group_by(comname, est_year) %>%
summarise(abund_21 = sum(abundance, na.rm = T),
biom_21 = sum(biom_adj, na.rm = T),
.groups = "keep") %>% ungroup()
# join them for side-by-side datar_
yr_df <- data.frame("est_year" = c("2008":"2017"))
combined_data <- yr_df %>%
left_join(summ_19, by = "est_year") %>%
left_join(summ_21, by = c("comname", "est_year")) %>%
mutate(
abund_change_19to21 = ((abund_21 - abund_19) / abund_19) * 100,
biom_change_19to21 = ((biom_21 - biom_19) / biom_19) * 100)
#return(combined_data)
# abundance correlation
abund_cor_19to21 <- cor(combined_data$abund_21,
combined_data$abund_19,
use = "pairwise.complete.obs")
# biomass correlation
biomass_cor_19to21 <- cor(combined_data$biom_21,
combined_data$biom_19,
use = "pairwise.complete.obs")
# average relative shift
abund_shift_19to21 <- mean(combined_data$abund_change_19to21, na.rm = T)
biom_shift_19to21 <- mean(combined_data$biom_change_19to21, na.rm = T)
# put in list to export
list("data" = combined_data,
"metrics" = data.frame(
"comname" = species_comname,
"svspp" = species_svspp,
"abund_corr_19to21" = abund_cor_19to21,
"biom_corr_19to21" = biomass_cor_19to21,
"perc_abund_19to21" = abund_shift_19to21,
"perc_biom_19to21" = biom_shift_19to21)
)
})For simplicity I then append the tables together so they are single data frames and not lists.
# put data and metrics into a table
comparison_data <- map_dfr(species_comparisons, ~.x[["data"]])
comparison_metrics <- map_dfr(species_comparisons, ~.x[["metrics"]]) Abundance/Biomass Comparisons
As a quick visual reference, here is how total annual abundance and biomass compare across data sources. The species have been split into groups to make paneling less crowded.
# How many species are there, and how many can we panel well - 84
#unique(andrew_species)
# make even number groups
even_groups <- list(
group_1 = sort(andrew_species)[1:17],
group_2 = sort(andrew_species)[18:34],
group_3 = sort(andrew_species)[35:51],
group_4 = sort(andrew_species)[52:68],
group_5 = sort(andrew_species)[69:84])Building Plots for Actual Abundance/Biomass
timeseries_plots <- map(even_groups, function(species_subset){
species_subset_dat <- comparison_data %>%
filter(comname %in% species_subset) %>%
mutate(comname = str_to_title(comname),
comname = fct_drop(comname),
comname = fct_inorder(comname))
# abundance plot
abundance_plot <- ggplot(species_subset_dat, aes(est_year)) +
geom_jitter(aes(y = abund_19, color = "Survdat Nye Allseason 2019"),
size = 0.8, height = 0, width = 0.05) +
geom_jitter(aes(y = abund_21, color = "Newest Survdat"),
size = 0.8, height = 0, width = 0.05) +
geom_line(aes(y = abund_19,
color = "Survdat Nye Allseason 2019",
linetype = "Survdat Nye Allseason 2019"),
group = 1, alpha = 0.7) +
geom_line(aes(y = abund_21,
color = "Newest Survdat",
linetype = "Newest Survdat"),
group = 1, alpha = 0.7) +
scale_y_continuous(labels = scales::comma_format()) +
scale_x_continuous(breaks = scales::pretty_breaks()) +
scale_color_gmri() +
facet_wrap(~comname, scales = "free") +
labs(x = "", y = "Annual Abundance",
color = "Survdat Retrieval",
linetype = "Survdat Retrieval") +
theme(legend.position = "none")
biomass_plot <- ggplot(species_subset_dat, aes(est_year)) +
geom_jitter(aes(y = biom_19, color = "Survdat Nye Allseason 2019"),
size = 0.8, height = 0, width = 0.1) +
geom_jitter(aes(y = biom_21, color = "Newest Survdat"),
size = 0.8, height = 0, width = 0.1) +
geom_line(aes(y = biom_19,
color = "Survdat Nye Allseason 2019",
linetype = "Survdat Nye Allseason 2019"),
group = 1, alpha = 0.7) +
geom_line(aes(y = biom_21,
color = "Newest Survdat",
linetype = "Newest Survdat"),
group = 1, alpha = 0.7) +
scale_y_continuous(labels = scales::comma_format()) +
scale_x_continuous(breaks = scales::pretty_breaks()) +
scale_color_gmri() +
facet_wrap(~comname, scales = "free") +
labs(x = "", y = "Annual Biomass",
color = "Survdat Retrieval",
linetype = "Survdat Retrieval") +
theme(legend.position = "bottom")
return(list(a_plot = abundance_plot, b_plot = biomass_plot,
stacked = (abundance_plot / biomass_plot)))
})Building Plots for Differences in Abundance/Biomass
timeseries_diff_plots <- map(even_groups, function(species_subset){
species_subset_dat <- comparison_data %>%
filter(comname %in% species_subset) %>%
mutate(comname = str_to_title(comname),
comname = fct_drop(comname),
comname = fct_inorder(comname))
# abundance plot
abundance_plot <- ggplot(species_subset_dat, aes(est_year)) +
geom_jitter(aes(y = abund_change_19to21, color = "Abundance Change 2019 to 2021"),
size = 0.8, height = 0, width = 0.1) +
geom_line(aes(y = abund_change_19to21,
color = "Abundance Change 2019 to 2021",
linetype = "Abundance Change 2019 to 2021"),
group = 1, alpha = 0.7) +
scale_y_continuous(labels = scales::comma_format()) +
scale_x_continuous(breaks = scales::pretty_breaks()) +
scale_color_gmri() +
facet_wrap(~comname, scales = "free") +
labs(x = "", y = "Difference in Annual Abundance",
color = "Survdat Retrieval Comparison",
linetype = "Survdat Retrieval Comparison") +
theme(legend.position = "none")
biomass_plot <- ggplot(species_subset_dat, aes(est_year)) +
geom_jitter(aes(y = biom_change_19to21, color = "Biomass Change 2019 to 2021"),
size = 0.8, height = 0, width = 0.1) +
geom_line(aes(y = biom_change_19to21,
color = "Biomass Change 2019 to 2021",
linetype = "Biomass Change 2019 to 2021"),
group = 1, alpha = 0.7) +
scale_y_continuous(labels = scales::comma_format()) +
scale_x_continuous(breaks = scales::pretty_breaks()) +
scale_color_gmri() +
facet_wrap(~comname, scales = "free") +
labs(x = "", y = "Difference in Annual Biomass",
color = "Survdat Retrieval Comparison",
linetype = "Survdat Retrieval Comparison") +
theme(legend.position = "bottom")
return(list(a_plot = abundance_plot, b_plot = biomass_plot,
stacked = (abundance_plot / biomass_plot)))
})Group 1
Observations
timeseries_plots$group_1$stacked
Differences
timeseries_diff_plots$group_1$stacked
Group 2
Observations
timeseries_plots$group_2$stacked
#### Differences
timeseries_diff_plots$group_2$stacked
Group 3
Observations
timeseries_plots$group_3$stacked
#### Differences
timeseries_diff_plots$group_3$stacked
Group 4
Observations
timeseries_plots$group_4$stacked
#### Differences
timeseries_diff_plots$group_4$stacked
Group 5
Observations
timeseries_plots$group_5$stacked
#### Differences
timeseries_diff_plots$group_5$stacked
Comparing 2019 to 2021
Abundance
Haddock Spotlight
haddock_data <- comparison_data %>%
filter(comname == "haddock")
ab_abund <- haddock_data %>%
ggplot(aes(est_year)) +
geom_line(aes(y = abund_19, color = "Survdat Nye Allseason 2019")) +
geom_line(aes(y = abund_21, color = "Newest Survdat")) +
scale_color_gmri() +
scale_y_continuous(labels = scales::comma_format()) +
scale_x_continuous(breaks = scales::pretty_breaks()) +
labs(x = "", y = "Abundance",
subtitle = "Haddock Absolute Abundance", color = "") +
theme(legend.position = "bottom")
p_change <- haddock_data %>%
ggplot(aes(est_year, abund_change_19to21)) +
geom_line() +
scale_x_continuous(breaks = scales::pretty_breaks()) +
labs(y = "Percent Change in Abundance\n2019 -> 2021", x = "")
ab_abund / p_change
Lobster Spotlight
lob_data <- comparison_data %>%
filter(comname == "american lobster")
ab_abund <- lob_data %>%
ggplot(aes(est_year)) +
geom_line(aes(y = abund_19, color = "Survdat Nye Allseason 2019")) +
geom_line(aes(y = abund_21, color = "Newest Survdat")) +
scale_color_gmri() +
scale_y_continuous(labels = scales::comma_format()) +
scale_x_continuous(breaks = scales::pretty_breaks()) +
labs(x = "", y = "Abundance",
subtitle = "American Lobster Absolute Abundance", color = "") +
theme(legend.position = "bottom")
p_change <- lob_data %>%
ggplot(aes(est_year, abund_change_19to21)) +
geom_line() +
scale_x_continuous(breaks = scales::pretty_breaks()) +
labs(y = "Percent Change in Abundance\n2019 -> 2021", x = "")
ab_abund / p_change
Problem Species
Poor Correlation
# correlation
corr_cutoff <- 0.8
corr_species <- comparison_metrics %>%
filter(abund_corr_19to21 <= corr_cutoff) %>%
mutate(comname = fct_drop(comname)) %>%
pull(comname)
# filter species
corr_sub <- comparison_data %>%
filter(comname %in% corr_species)
# how many species per plot
p1 <- corr_species[1:6]
p2 <- corr_species[-c(1:6)]
# p1
corr_sub %>%
filter(comname %in% p1) %>%
mutate(comname = fct_drop(comname)) %>%
ggplot() +
geom_line(aes(est_year, abund_19, color = "Survdat Nye Allseason 2019")) +
geom_line(aes(est_year, abund_21, color = "Newest Survdat")) +
facet_wrap(~comname, ncol = 1, scales = "free" ) +
scale_y_continuous(labels = scales::comma_format()) +
scale_x_continuous(breaks = scales::pretty_breaks()) +
scale_color_gmri() +
labs(x = "",
y = "Annual Abundance",
title = paste0("Species with Annual Abundance Correlation <= ", corr_cutoff),
color = "Survdat Source",
subtitle = "Subset 1")
# p1
corr_sub %>%
filter(comname %in% p2) %>%
mutate(comname = fct_drop(comname)) %>%
ggplot() +
geom_line(aes(est_year, abund_19, color = "Survdat Nye Allseason 2019")) +
geom_line(aes(est_year, abund_21, color = "Newest Survdat")) +
facet_wrap(~comname, ncol = 1, scales = "free" ) +
scale_y_continuous(labels = scales::comma_format()) +
scale_x_continuous(breaks = scales::pretty_breaks()) +
scale_color_gmri() +
labs(x = "",
y = "Annual Abundance",
title = paste0("Species with Annual Abundance Correlation <= ", corr_cutoff),
color = "Survdat Source",
subtitle = "Subset 2")
Large Percent Change
# percent change
perc_cutoff <- 25
perc_species <- comparison_metrics %>%
filter(perc_abund_19to21 >= perc_cutoff) %>%
mutate(comname = fct_drop(comname)) %>%
pull(comname)
comparison_data %>%
filter(comname %in% perc_species) %>%
mutate(comname = fct_drop(comname)) %>%
ggplot() +
geom_line(aes(est_year, abund_19, color = "Survdat Nye Allseason 2019")) +
geom_line(aes(est_year, abund_21, color = "Newest Survdat")) +
facet_wrap(~comname, ncol = 1, scales = "free") +
scale_y_continuous(labels = scales::comma_format()) +
scale_x_continuous(breaks = scales::pretty_breaks()) +
scale_color_gmri() +
labs(x = "",
y = "Annual Abundance",
title = paste0("Species with Avg. Shift in Abundance >= ", perc_cutoff, "%"),
color = "Survdat Source")
Biomass
Haddock Spotlight
ab_biom <- haddock_data %>%
ggplot(aes(est_year)) +
geom_line(aes(y = biom_19, color = "Survdat Nye Allseason 2019")) +
geom_line(aes(y = biom_21, color = "Newest Survdat")) +
scale_color_gmri() +
scale_y_continuous(labels = scales::comma_format()) +
labs(x = "", y = "Biomass", subtitle = "Haddock Absolute Biomass", color = "") +
theme(legend.position = "bottom")
p_change <- haddock_data %>%
ggplot(aes(est_year, biom_change_19to21)) +
geom_line() +
labs(y = "Percent Change in Biomass\n2019 -> 2021", x = "")
ab_biom / p_change
Lobster Spotlight
ab_biom <- lob_data %>%
ggplot(aes(est_year)) +
geom_line(aes(y = biom_19, color = "Survdat Nye Allseason 2019")) +
geom_line(aes(y = biom_21, color = "Newest Survdat")) +
scale_color_gmri() +
scale_y_continuous(labels = scales::comma_format()) +
labs(x = "", y = "Biomass", subtitle = "American Lobster Absolute Biomass", color = "") +
theme(legend.position = "bottom")
p_change <- lob_data %>%
ggplot(aes(est_year, biom_change_19to21)) +
geom_line() +
labs(y = "Percent Change in Biomass\n2019 -> 2021", x = "")
ab_biom / p_change
Problem Species
Poor Correlation
# correlation
corr_cutoff <- 0.8
corr_species <- comparison_metrics %>%
filter(biom_corr_19to21 <= corr_cutoff) %>%
mutate(comname = fct_drop(comname)) %>%
pull(comname)
# plot
if(length(corr_species) > 0) {
comparison_data %>%
filter(comname %in% corr_species) %>%
mutate(comname = fct_drop(comname)) %>%
ggplot() +
geom_line(aes(est_year, biom_19, color = "Survdat Nye Allseason 2019")) +
geom_line(aes(est_year, biom_21, color = "Newest Survdat")) +
facet_wrap(~comname, ncol = 1, scales = "free" ) +
scale_y_continuous(labels = scales::comma_format()) +
scale_x_continuous(breaks = scales::pretty_breaks()) +
scale_color_gmri() +
labs(x = "",
y = "Annual Biomass",
title = paste0("Species with Annual Biomass Correlation <= ", corr_cutoff),
color = "Survdat Source")
}
Large Percent Change
# percent change
perc_cutoff <- 25
perc_species <- comparison_metrics %>%
filter(perc_biom_19to21 >= perc_cutoff) %>%
mutate(comname = fct_drop(comname)) %>%
pull(comname)
comparison_data %>%
filter(comname %in% perc_species) %>%
mutate(comname = fct_drop(comname)) %>%
ggplot() +
geom_line(aes(est_year, biom_19, color = "Survdat Nye Allseason 2019")) +
geom_line(aes(est_year, biom_21, color = "Newest Survdat")) +
facet_wrap(~comname, ncol = 1, scales = "free") +
scale_y_continuous(labels = scales::comma_format()) +
scale_x_continuous(breaks = scales::pretty_breaks()) +
scale_color_gmri() +
labs(x = "",
y = "Annual Biomass",
title = paste0("Species with Avg. Shift in Biomass >= ", perc_cutoff, "%"),
color = "Survdat Source")
A work by Adam A. Kemberling
Akemberling@gmri.org